Frequency and nature of medical emergency team afferent limb failure in patients with documented vital sign abnormalities: A retrospective point prevalence study.

BACKGROUND: Medical emergency team (MET) afferent limb failure is the presence of MET triggers and the absence of a documented MET call. OBJECTIVES: The aim of this study was to measure and understand the frequency and nature of MET afferent limb failure in patients with documented vital sign abnormalities in an Australian major teaching hospital. METHODS: A retrospective point prevalence study was conducted at a 600-bed teaching hospital in Melbourne, Australia. Data were collected for all adult inpatients (aged ≥18 years) on 13 wards (three general medicine, three surgical, and seven specialist wards) during a randomly selected 24-h period. Data were extracted from the electronic medical record. RESULTS: There were 357 patients included in the study, with a median age of 72 y. Of the 9716 vital sign measures extracted, 0.9% fulfilled patient-specific MET activation criteria. There were 93 MET triggers documented in 36 patients: 25 patients experienced MET afferent limb failure. The major issues related to MET afferent limb failure were MET trigger modification processes, resolution of vital sign abnormalities, alternative escalation of care, and limitations of medical treatment orders without specific modifications to MET triggers. CONCLUSIONS: Mandating MET activation for one aberrant vital sign at a single point in time warrants further assessment: lack of timely vital sign resolution may be a more appropriate trigger for MET calls and should be formally tested in future research. The frequency and effectiveness of alternative escalation pathways and local management of patients with MET triggers also warrant further investigation.

Hospital-acquired complications: the relative importance of hospital- and patient-related factors.

OBJECTIVE: To quantify the prevalence of hospital-acquired complications; to determine the relative influence of patient- and hospital-related factors on complication rates. DESIGN, PARTICIPANTS: Retrospective analysis of administrative data (Integrated South Australian Activity Collection; Victorian Admitted Episodes Dataset) for multiple-day acute care episodes for adults in public hospitals. SETTING: Thirty-eight major public hospitals in South Australia and Victoria, 2015-2018. MAIN OUTCOME MEASURES: Hospital-acquired complication rates, overall and by complication class, by hospital and hospital type (tertiary referral, major metropolitan service, major regional service); variance in rates (intra-class correlation coefficient, ICC) at the patient, hospital, and hospital type levels as surrogate measures of their influence on rates. RESULTS: Of 1 558 978 public hospital episodes (10 029 918 bed-days), 151 486 included a total of 214 286 hospital-acquired complications (9.72 [95% CI, 9.67-9.77] events per 100 episodes; 2.14 [95% CI, 2.13-2.15] events per 100 bed-days). Complication rates were highest in tertiary referral hospitals (12.7 [95% CI, 12.6-12.8] events per 100 episodes) and for episodes including intensive care components (37.1 [95% CI, 36.7-37.4] events per 100 episodes). For all complication classes, inter-hospital variation was determined more by patient factors (overall ICC, 0.55; 95% CI, 0.53-0.57) than by hospital factors (ICC, 0.04; 95% CI, 0.02-0.07) or hospital type (ICC, 0.01; 95% CI, 0.001-0.03). CONCLUSIONS: Hospital-acquired complications were recorded for 9.7% of hospital episodes, but patient-related factors played a greater role in determining their prevalence than the treating hospital.

Clinical evaluation of the national hospital-acquired complication programme.

BACKGROUND: The national hospital-acquired complication programme captures complications arising from patient-related and hospital-related factors, but the proportion of the two is unclear. AIM: Health services are encouraged to evaluate data from the national hospital-acquired complications (HAC) programme and identify strategies to mitigate them. METHODS: A retrospective chart review compared HAC extracted from administrative data. The setting was a 430-bed university-affiliated metropolitan hospital. Records from 260 participants with, and 462 without, reported HAC from 2619 multi-day stay adults were reviewed. The main outcome measures were prevalence and positive predictive value (PPV) of HAC methodology. RESULTS: No errors of HAC coding or classification were identified. Four hundred and twenty-three HAC events were reported in 260 records; most commonly delirium (n = 57; 13.4%), pneumonia (n = 46; 10.9%), blood stream infection (n = 39; 9.2%), hypoglycaemia (n = 33; 7.8%) and cardiac arrhythmias (n = 33; 7.8%). One hundred and eight (25.5%) 'HAC' events in 69 separations (95% confidence interval (CI) = 2.05-3.33 per 100 separations) were false positive, and 43 of 462 (95% CI = 6.72-12.22 per 100 separations) were false negative. Prevalence of total (reported plus missing) HAC was 16.06 (95% CI = 14.02-19.52), reported HAC was 9.93 (95% CI = 8.76-11.21), potentially preventable HAC was 1.68 (95% CI = 1.22-2.26) and healthcare errors was 0.31 (95% CI = 0.13-1.30) per 100 separations. PPV of HAC for true clinical events was 0.74 (0.68-0.79), preventable events 0.18 (0.13-0.23) and healthcare error 0.03 (0.01-0.06). CONCLUSIONS: Prevalence of HAC events was higher than expected, but PPV for healthcare errors was low, suggesting provision of care is a less common cause of HAC events than patient factors. HAC may be an indicator of hospital admission complexity rather than HAC.

Intensive care patients receiving vasoactive medications: A retrospective cohort study.

BACKGROUND: Vasoactive medications are high-risk drugs commonly used in intensive care units (ICUs), which have wide variations in clinical management. OBJECTIVES: The aim of this study was to describe the patient population, treatment, and clinical characteristics of patients who did and did not receive vasoactive medications while in the ICU and to develop a predictive tool to identify patients needing vasoactive medications. METHODS: A retrospective cohort study of patients admitted to a level three tertiary referral ICU over a 12-month period from October 2018 to September 2019 was undertaken. Data from electronic medical records were analysed to describe patient characteristics in an adult ICU. Chi square and Mann-Whitney U tests were used to analyse data relating to patients who did and did not receive vasoactive medications. Univariate analysis and Pearson's r2 were used to determine inclusion in multivariable logistic regression. RESULTS: Of 1276 patients in the cohort, 40% (512/1276) received a vasoactive medication for haemodynamic support, with 84% (428/512) receiving noradrenaline. Older patients (odds ratio [OR] = 1.02; 95% confidence interval [CI] = 1.01-1.02; p < 0.001) with higher Acute Physiology and Chronic Health Evaluation (APACHE) III scores (OR = 1.04; 95% CI = 1.03-1.04; p < 0.001) were more likely to receive vasoactive medications than those not treated with vasoactive medications during an intensive care admission. A model developed using multivariable analysis predicted that patients admitted with sepsis (OR = 2.43; 95% CI = 1.43-4.12; p = 0.001) or shock (OR = 4.05; 95% CI = 2.68-6.10; p < 0.001) and managed on mechanical ventilation (OR = 3.76; 95% CI = 2.81-5.02; p < 0.001) were more likely to receive vasoactive medications. CONCLUSIONS: Mechanically ventilated patients admitted to intensive care for sepsis and shock with higher APACHE III scores were more likely to receive vasoactive medications. Predictors identified in the multivariable model can be used to direct resources to patients most at risk of receiving vasoactive medications.